To train QuReTeC using the standard binary cross entropy loss, we need a dataset containing a gold binary label for each \(q \in \mathcal {H}_i\) . Since such labels are in general not available, we have to resort to distant supervision methods [1]} to derive these labels from document-level relevance labels, which are widely available, based on how a term influences the document retrieval accuracy with respect to a query when the term is used to expand the query [2]}.
